| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 65 | | tagDensity | 0.062 | | leniency | 0.123 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2248 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 88.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2248 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "stomach" | | 1 | "silence" | | 2 | "weight" | | 3 | "pulse" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 196 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 196 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 257 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2248 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 81 | | wordCount | 1845 | | uniqueNames | 19 | | maxNameDensity | 2.01 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 37 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Elias | 6 | | Venn | 2 | | Holborn | 1 | | Raven | 1 | | Nest | 1 | | Saint | 2 | | Christopher | 2 | | Herrera | 1 | | Tomás | 11 | | Dickens | 1 | | Poplar | 1 | | Soho | 2 | | London | 1 | | Silas | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Elias" | | 3 | "Venn" | | 4 | "Raven" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Herrera" | | 8 | "Tomás" | | 9 | "Silas" |
| | places | | 0 | "Charing" | | 1 | "Cross" | | 2 | "Road" | | 3 | "Poplar" | | 4 | "Soho" | | 5 | "London" |
| | globalScore | 0.497 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 136 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.445 | | wordCount | 2248 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 257 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 157 | | mean | 14.32 | | std | 14.75 | | cv | 1.03 | | sampleLengths | | 0 | 45 | | 1 | 3 | | 2 | 41 | | 3 | 10 | | 4 | 38 | | 5 | 17 | | 6 | 2 | | 7 | 58 | | 8 | 13 | | 9 | 29 | | 10 | 1 | | 11 | 8 | | 12 | 11 | | 13 | 60 | | 14 | 2 | | 15 | 16 | | 16 | 53 | | 17 | 6 | | 18 | 4 | | 19 | 10 | | 20 | 4 | | 21 | 28 | | 22 | 9 | | 23 | 54 | | 24 | 15 | | 25 | 45 | | 26 | 36 | | 27 | 5 | | 28 | 10 | | 29 | 5 | | 30 | 15 | | 31 | 4 | | 32 | 15 | | 33 | 3 | | 34 | 15 | | 35 | 41 | | 36 | 2 | | 37 | 29 | | 38 | 5 | | 39 | 6 | | 40 | 5 | | 41 | 4 | | 42 | 2 | | 43 | 13 | | 44 | 42 | | 45 | 8 | | 46 | 8 | | 47 | 49 | | 48 | 22 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 196 | | matches | | 0 | "was headed" | | 1 | "been scratched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 290 | | matches | | 0 | "was going" | | 1 | "was running" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 257 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1850 | | adjectiveStacks | 1 | | stackExamples | | 0 | "black behind rain-speckled windows." |
| | adverbCount | 50 | | adverbRatio | 0.02702702702702703 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0005405405405405405 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 257 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 257 | | mean | 8.75 | | std | 6.7 | | cv | 0.766 | | sampleLengths | | 0 | 16 | | 1 | 29 | | 2 | 3 | | 3 | 15 | | 4 | 26 | | 5 | 10 | | 6 | 14 | | 7 | 4 | | 8 | 6 | | 9 | 8 | | 10 | 6 | | 11 | 17 | | 12 | 2 | | 13 | 13 | | 14 | 8 | | 15 | 5 | | 16 | 8 | | 17 | 24 | | 18 | 13 | | 19 | 10 | | 20 | 4 | | 21 | 15 | | 22 | 1 | | 23 | 6 | | 24 | 2 | | 25 | 1 | | 26 | 10 | | 27 | 35 | | 28 | 25 | | 29 | 2 | | 30 | 9 | | 31 | 7 | | 32 | 15 | | 33 | 22 | | 34 | 16 | | 35 | 6 | | 36 | 4 | | 37 | 10 | | 38 | 4 | | 39 | 4 | | 40 | 10 | | 41 | 14 | | 42 | 9 | | 43 | 9 | | 44 | 10 | | 45 | 14 | | 46 | 10 | | 47 | 11 | | 48 | 15 | | 49 | 8 |
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| 58.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.36964980544747084 | | totalSentences | 257 | | uniqueOpeners | 95 | |
| 98.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 169 | | matches | | 0 | "Then a witness from a" | | 1 | "Somewhere deeper, an animal cried" | | 2 | "Then he passed her the" | | 3 | "Somewhere in that underworld, Elias" | | 4 | "Then she went down." |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 169 | | matches | | 0 | "He did not look back" | | 1 | "He knew where he was" | | 2 | "She crossed the street through" | | 3 | "Her leather watch clung damp" | | 4 | "Her voice bounced off the" | | 5 | "She had spent two weeks" | | 6 | "She drove after him, shoulder" | | 7 | "He cut under the green" | | 8 | "His eyes took her in," | | 9 | "He wore stillness like armour." | | 10 | "Her eyes landed on the" | | 11 | "She looked at the barman." | | 12 | "He set the glass down." | | 13 | "He gave her a quick," | | 14 | "She pointed at the bookshelf." | | 15 | "His jaw shifted once." | | 16 | "He gave a short laugh" | | 17 | "She gripped one side and" | | 18 | "It smelled of wet stone," | | 19 | "You ever get the feeling" |
| | ratio | 0.183 | |
| 63.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 134 | | totalSentences | 169 | | matches | | 0 | "Rain slapped the windscreen hard" | | 1 | "Detective Harlow Quinn leaned over" | | 2 | "The radio on the passenger" | | 3 | "Quinn snatched her coat from" | | 4 | "The rain found the gap" | | 5 | "The man in the blue" | | 6 | "He did not look back" | | 7 | "That told Quinn more than" | | 8 | "He knew where he was" | | 9 | "She crossed the street through" | | 10 | "A courier on a bike" | | 11 | "Quinn hit the opposite pavement" | | 12 | "Water slapped up her trousers." | | 13 | "Her leather watch clung damp" | | 14 | "Quinn took the corner fast" | | 15 | "The lane narrowed into a" | | 16 | "The fugitive’s trainers smacked the" | | 17 | "Her voice bounced off the" | | 18 | "She had spent two weeks" | | 19 | "Nobody in her unit had" |
| | ratio | 0.793 | |
| 59.17% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 169 | | matches | | 0 | "Now she stood in a" | | 1 | "By the time she dragged" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 79 | | technicalSentenceCount | 3 | | matches | | 0 | "She had spent two weeks on Elias Venn, if that was his real name, and every hour had peeled back another layer of filth: burner phones, cash drops, dead-end she…" | | 1 | "The music carried on, tinny and stubborn, under the silence that had settled over the bar." | | 2 | "Three days later, he had died in a warehouse with no sign of forced entry, no prints but his own and hers, and burns on the concrete that forensics could not ex…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Tomás muttered (mutter)" |
| | dialogueSentences | 65 | | tagDensity | 0.062 | | leniency | 0.123 | | rawRatio | 0.25 | | effectiveRatio | 0.031 | |